Climate shapes community flowering periods across biomes

Aim: Climate shapes the composition and function of plant communities globally, but it remains unclear how this influence extends to floral traits. Flowering phenology, or the time period in which a species flowers, has well-studied relationships with climatic signals at the species level but has rarely been explored at a cross-community and continental scale. Here, we characterise the distribution of flowering periods (months of flowering) across continental plant communities encompassing six biomes, and determine the influence of climate on community flowering period lengths. Location: Australia. Taxon: Flowering plants. Methods: We combined plant composition and abundance data from 629 standardised floristic surveys (AusPlots) with data on flowering period from the AusTraits database and additional primary literature for 2983 species. We assessed abundance-weighted community mean flowering periods across biomes and tested their relationship with climatic annual means and the predictability of climate conditions using regression models. Results: Combined, temperature and precipitation (annual mean and predictability) explain 29% of variation in continental community flowering period. Plant communities with higher mean temperatures and lower mean precipitation have longer mean flowering periods. Moreover, plant communities in climates with predictable temperatures and, to a lesser extent, predictable precipitation have shorter mean flowering periods. Flowering period varies by biome, being longest in deserts and shortest in alpine and montane communities. For instance, desert communities experience low and unpredictable precipitation and high, unpredictable temperatures and have longer mean flowering periods, with desert species typically flowering at any time of year in response to rain. Main conclusions: Current climate conditions shape flowering periods across biomes, with implications for phenology under climate change. Shifts in flowering periods across climatic gradients reflect changes in plant strategies, affecting patterns of plant growth and reproduction as well as the availability of floral resources for pollinators across the landscape

Unlocking drought-induced tree mortality : physiological mechanisms to modeling

Drought-related tree mortality has become a major concern worldwide due to its pronounced negative impacts on the functioning and sustainability of forest ecosystems. However, our ability to identify the species that are most vulnerable to drought, and to pinpoint the spatial and temporal patterns of mortality events, is still limited. Model is useful tools to capture the dynamics of vegetation at spatiotemporal scales, yet contemporary land surface models (LSMs) are often incapable of predicting the response of vegetation to environmental perturbations with sufficient accuracy, especially under stressful conditions such as drought. Significant progress has been made regarding the physiological mechanisms underpinning plant drought response in the past decade, and plant hydraulic dysfunction has emerged as a key determinant for tree death due to water shortage. The identification of pivotal physiological events and relevant plant traits may facilitate forecasting tree mortality through a mechanistic approach, with improved precision. In this review, we (1) summarize current understanding of physiological mechanisms leading to tree death, (2) describe the functionality of key hydraulic traits that are involved in the process of hydraulic dysfunction, and (3) outline their roles in improving the representation of hydraulic function in LSMs. We urge potential future research on detailed hydraulic processes under drought, pinpointing corresponding functional traits, as well as understanding traits variation across and within species, for a better representation of drought-induced tree mortality in models

Predictability of precipitation over the conterminous U.S. based on the CMIP5 multi-model ensemble

Characterizing precipitation seasonality and variability in the face of future uncertainty is important for a well-informed climate change adaptation strategy. Using the Colwell index of predictability and monthly normalized precipitation data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensembles, this study identifies spatial hotspots of changes in precipitation predictability in the United States under various climate scenarios. Over the historic period (1950–2005), the recurrent pattern of precipitation is highly predictable in the East and along the coastal Northwest, and is less so in the arid Southwest. Comparing the future (2040–2095) to the historic period, larger changes in precipitation predictability are observed under Representative Concentration Pathways (RCP) 8.5 than those under RCP 4.5. Finally, there are region-specific hotspots of future changes in precipitation predictability, and these hotspots often coincide with regions of little projected change in total precipitation, with exceptions along the wetter East and parts of the drier central West. Therefore, decision-makers are advised to not rely on future total precipitation as an indicator of water resources. Changes in precipitation predictability and the subsequent changes on seasonality and variability are equally, if not more, important factors to be included in future regional environmental assessment

Characterizing predictability of precipitation means and extremes in the conterminous United States, 1949-2010

The proper understanding of precipitation variability, seasonality, and predictability are important for effective environmental management. Precipitation and its associated extremes vary in magnitude and duration both spatially and temporally, making it one of the most challenging climate parameters to predict on the basis of global and regional climate models. Using information theory, an improved understanding of precipitation predictability in the conterminous United States over the period of 1949–2010 is sought based on a gridded monthly precipitation dataset. Predictability is defined as the recurrent likelihood of patterns described by the metrics of magnitude variability and seasonality. It is shown that monthly mean precipitation and duration-based dry and wet extremes are generally highly variable in the east compared to those in the west, while the reversed spatial pattern is observed for intensity-based wetness indices except along the Pacific Northwest coast. It is thus inferred that, over much of the U.S. landscape, variations of monthly mean precipitation are driven by the variations in precipitation occurrences rather than the intensity of infrequent heavy rainfall. It is further demonstrated that precipitation seasonality for means and extremes is homogeneously invariant within the United States, with the exceptions of the West Coast, Florida, and parts of the Midwest, where stronger seasonality is identified. A proportionally higher role of variability in regulating precipitation predictability is demonstrated. Seasonality surpasses variability only in parts of the West Coast. The quantified patterns of predictability for precipitation means and extremes have direct applications to those phenomena influenced by climate periodicity, such as biodiversity and ecosystem management

The relationships of extreme precipitation and temperature events with ethnographic reports of droughts and floods in nonindustrial societies

Our broad research goal is to understand how human societies adapt to natural hazards, such as droughts and floods, and how their social and cultural structures are shaped by these events. Here we develop meteorological data of extreme dry, wet, cold, and warm indices relative to 96 largely nonindustrial societies in the worldwide Standard Cross-Cultural Sample to explore how well the meteorological data can be used to hindcast ethnographically reported drought and flood events and the global patterns of extremes. We find that the drought indices that are best at hindcasting ethnographically reported droughts [precipitation minus evaporation (P − E) measures] also tend to overpredict the number of droughts, and therefore we propose a combination of these two indices plus the PDSI as an optimal approach. Some wet precipitation indices (R10S and R20S) are more effective at hindcasting ethnographically reported floods than others. We also calculate the predictability of those extreme indices and use factor analysis to reduce the number of variables so as to discern global patterns. This work highlights the ability to use extreme meteorological indices to fill in gaps in ethnographic records; in the future, this may help us to determine relationships between extreme events and societal response over longer time scales than are otherwise available

Convergence of carbon sink magnitude and water table depth in global wetlands

Wetlands are strategic areas for carbon uptake, but accurate assessments of their sequestration ability are limited by the uncertainty and variability in their carbon balances. Based on 2385 observations of annual net ecosystem production from global wetlands, we show that the mean net carbon sinks of inland wetlands, peatlands and coastal wetlands are 0.57, 0.29 and 1.88 tons of carbon per hectare per year, respectively, with a mean value of 0.57 tons of carbon per hectare per year weighted by the distribution area of different wetland types. Carbon sinks are mainly in Asia and North America. Within and across wetland types, we find that water table depth (WTD) exerts greater control than climate- and ecosystem-related variables, and an increase in WTD results in a stronger carbon sink. Our results highlight an urgent need to sustain wetland hydrology under global change; otherwise, wetlands are at high risk of becoming carbon sources to the atmosphere

Climate variability, drought, and the belief that high gods are associated with weather in nonindustrial societies

All societies have religious beliefs, but societies vary widely in the number and type of gods in which they believe as well as their ideas about what the gods do. In many societies, a god is thought to be responsible for weather events. In some of those societies, a god is thought to cause harm with weather and/or can choose to help, such as by bringing needed rain. In other societies, gods are not thought to be involved with weather. Using a worldwide, largely nonindustrial sample of 46 societies with high gods, this research explores whether certain climate patterns predict the belief that high gods are involved with weather. Our major expectation, largely supported, was that such beliefs would most likely be found in drier climates. Cold extremes and hot extremes have little or no relationship to the beliefs that gods are associated with weather. Since previous research by Skoggard et al. showed that greater resource stress predicted the association of high gods with weather, we also tested mediation path models to help us evaluate whether resource stress might be the mediator explaining the significant associations between drier climates and high god beliefs. The climate variables, particularly those pertaining to dryness, continue to have robust relationships to god beliefs when controlling on resource stress; at best, resource stress has only a partial mediating effect. We speculate that drought causes humans more anxiety than floods, which may result in the greater need to believe supernatural beings are not only responsible for weather but can help humans in times of need

Biome-specific climatic space defined by temperature and precipitation predictability

Aim: Global biomes are often classified by mean annual temperature and precipitation, but there is significant overlap between biomes, making it difficult to interpret the role of climate in the distribution of biomes globally. Climate predictability (including long-term reliability of both seasonality and inter-annual variability) varies considerably between biomes and regulates biodiversity distribution, adaptation and evolution, but its global pattern has rarely been investigated. The aim of this study was to characterize climatic space quantitatively for major biomes of the world using temperature and precipitation predictability, and to interpret its biological implications under future climate change. Methods: We calculated global gridded temperature and precipitation predictability based on an information theory approach, and compared climatic spaces defined by these measures within and across biomes. Results: We show that temperature predictability has a clear latitudinal gradient, whereas precipitation predictability is geographically variable. We further show that temperature and precipitation predictability form distinct climatic spaces for major biomes across the globe, and importantly, temperature and precipitation predictability can robustly distinguish biomes that are overlapping in mean annual climate statistics. Main conclusions: Climatic space created by temperature and precipitation predictability supplements the traditional biome-specific climatic spaces created by annual mean temperature and total precipitation. Quantifying measures of climate predictability helps us to understand adaptation strategies adopted by local organisms within and across biomes. Our results show that quantifying climate predictability is a simple and effective way to delineate more robustly biome climate space and its influences on macroecology and evolutionary biology, which in turn could underpin conservation efforts under future climate change, especially when prevailing climates are comparable in terms of magnitude

Biome‐specific climatic space defined by temperature and precipitation predictability

Aim: Global biomes are often classified by mean annual temperature and precipitation, but there is significant overlap between biomes, making it difficult to interpret the role of climate in the distribution of biomes globally. Climate predictability (including long-term reliability of both seasonality and inter-annual variability) varies considerably between biomes and regulates biodiversity distribution, adaptation and evolution, but its global pattern has rarely been investigated. The aim of this study was to characterize climatic space quantitatively for major biomes of the world using temperature and precipitation predictability, and to interpret its biological implications under future climate change. Methods: We calculated global gridded temperature and precipitation predictability based on an information theory approach, and compared climatic spaces defined by these measures within and across biomes. Results: We show that temperature predictability has a clear latitudinal gradient, whereas precipitation predictability is geographically variable. We further show that temperature and precipitation predictability form distinct climatic spaces for major biomes across the globe, and importantly, temperature and precipitation predictability can robustly distinguish biomes that are overlapping in mean annual climate statistics. Main conclusions: Climatic space created by temperature and precipitation predictability supplements the traditional biome-specific climatic spaces created by annual mean temperature and total precipitation. Quantifying measures of climate predictability helps us to understand adaptation strategies adopted by local organisms within and across biomes. Our results show that quantifying climate predictability is a simple and effective way to delineate more robustly biome climate space and its influences on macroecology and evolutionary biology, which in turn could underpin conservation efforts under future climate change, especially when prevailing climates are comparable in terms of magnitude

Towards a more physiological representation of vegetation phosphorus processes in land surface models

Our ability to understand the effect of nutrient limitation on ecosystem productivity is key to the prediction of future terrestrial carbon storage. Significant progress has been made to include phosphorus (P) cycle processes in land surface models (LSMs), but these efforts are focused on the soil component of the P cycle. Incorporating the soil component is important to estimate plant‐available P, but does not necessarily address the vegetation response to P limitation or plant–soil interactions. A more detailed representation of plant P processes is needed to link nutrient availability and ecosystem productivity. We review physiological and biochemical evidence for vegetation responses to P availability, and recommend ways to move towards a more physiological representation of vegetation P processes in LSMs